SCIEN Talk

Combining photography and spectroscopy, spectral imaging enables us to see what no traditional color camera has seen before. The current trend is to miniaturize the technology and bring it towards industry. In this talk, I will first give a general introduction to the most common pitfalls of spectral imaging and the challenges that come with miniaturization. Major pitfalls include balancing cross-talk, quantum efficiency, illumination and the optics. Miniaturization has become possible thanks to the monolithic per-pixel integration of thin-film Fabry-Pérot filters on CMOS imaging sensors. I will explain the difficulty of using these cameras with non- telecentric lenses. This is a major concern because of the angular dependency of the thin-film filters. I will demonstrate how this important issue can be solved using a model-based approach.

Deep learning algorithms offer a powerful means to automatically analyze the content of biomedical images. However, many biological samples of interest are difficult to resolve with a standard optical microscope. Either they are too large to fit within the microscope's field-of-view, or too thick, or are quickly moving around. In this talk, I will discuss our recent work in addressing these challenges by using deep learning algorithms to design new experimental strategies for microscopic imaging. Specifically, we use deep neural networks to jointly optimize the physical parameters of our computational microscopes - their illumination settings, lens layouts and data transfer pipelines, for example - for specific tasks. Examples include learning specific illumination patterns that can improve classification of the malaria parasite by up to 15%, and establishing fast methods to automatically track moving specimens across gigapixel-sized images.

Individuals of all genders invited to be a part of:
#StanfordToo: A Conversation about Sexual Harassment in Our Academic Spaces, where we will feature real stories of harassment at Stanford academic STEM in a conversation with Provost Drell, Dean Minor (SoM), and Dean Graham (SE3). We will have plenty of time for audience discussion on how we can take concrete action to dismantle this culture and actively work towards a more inclusive Stanford for everyone. While our emphasis is on STEM fields, we welcome and encourage participation from students, postdocs, staff, and faculty of all academic disciplines and backgrounds.

Handbooks are an essential requirement for understanding and using many artifacts found in our daily life. We use handbooks to understand how things work and how to maintain them. Most handbooks still exist on paper relying on graphical illustrations and accompanying textual explanations to convey the relevant information to the reader. With the success of video sharing platforms a large body of video tutorials available for nearly every aspect of life became available. Video tutorials can often expand printed handbooks with the demonstrations of actions required to solve certain tasks. However, interpreting printed manuals and video tutorials often requires a certain mental effort since users have to match printed images or video frames with the physical object in their environment.

Augmented Reality (AR) has been demonstrated to be effective of presenting information traditionally provided in printed handbooks and video tutorials. However, creating interactive illustrative graphics for AR is costly and requires specially trained authors. In this this talk, I will present research towards the automation of the authoring process of AR handbooks by interactively retargeting conventional, two-dimensional image and video data into three-dimensional AR handbooks. In addition, I will present interaction, visualization and rendering techniques tailored for AR handbooks.

Light develops computational imaging technologies that utilize heterogenous constellations of small cameras to create sophisticated imaging effects. This enables the company to provide hardware solutions that are compact – they can easily fit into a cell phone, or a similar small form factor. In this talk, I will review the recent progress of computational imaging research done at the company.

Photonics has always been the technology of the future. Light is faster, can multiplex etc. have all been "good" arguments for several decades and the ushering in of optical computing has perpetually been just a few years away. However, over the last decade, with the advent of micro-and nanofabrication techniques and phenomenal advances in photonics, that era seems to have finally arrived. The ability to create integrated optical circuits on a chip is near. But (and yes, there's always a but) you need "functional" materials that can be used to control and manipulate this flow of information. In electronics, doping silicon results in one of the most versatile functional materials ever employed by humanity. And that can used to efficiently route electrical signals. How do you do that optically? I hope to convince you that whatever route photonics takes, a class of materials known as phase change materials, will play a key role in its commercialization. These materials can be addressed electrically, and whilst this can be used to control optical signals on photonic circuits this can also be used to create displays and smart windows. In this talk, I hope to give a whistle-stop tour of these applications of these materials with a view towards their near-term applications in displays, and their longer-term potential ranging from integrated photonic memories to machine-learning hardware components.

Active 3D imaging systems, such as LIDAR, are becoming increasingly prevalent for applications in autonomous vehicle navigation, remote sensing, human-computer interaction, and more. These imaging systems capture distance by directly measuring the time it takes for short pulses of light to travel to a point and return. With emerging sensor technology we can detect down to single arriving photons and identify their arrival at picosecond timescales, enabling new and exciting imaging modalities. In this talk, I discuss trillion-frame-per-second imaging, efficient depth imaging with sparse photon detections, and imaging objects hidden from direct line of sight.

3D Computer Vision (3D Vision) techniques have been the key solutions to various scene perception problems such as depth from image(s), camera/object pose estimation, localization and 3D reconstruction of a scene. These solutions are the major part of many AI applications including AR/VR, autonomous driving and robotics. In this talk, I will first review several categories of 3D Vision problems and their challenges. Given the category of static scene perception, I will introduce several learning-based depth estimation methods such as PlaneRCNN, Neural RGBD, and camera pose estimation methods including MapNet as well as few registration algorithms deployed in NVIDIA's products. I will then introduce more challenging real world scenarios where scenes contain non-stationary rigid changes, non-rigid motions, or varying appearance due to the reflectance and lighting changes, which can cause scene reconstruction to fail due to the view dependent properties. I will discuss several solutions to these problems and conclude by summarizing the future directions for 3D Vision research that are being conducted by NVIDIA's learning and perception research (LPR) team.

Cancer is a surgically treated disease; almost 80% of early stage solid tumors undergo surgery at some point in their treatment course. The biggest gap in quality remains the high rate of tumor-positive margins in surgical resections. The biggest barrier is that only a limited amount of the tissue can be sampled for frozen section analysis (< 5%). The biggest challenge is to develop equipment to direct frozen section analysis to the most area on the specimen most likely to contain a positive margin. To this end, we developed intraoperative devices to leverage molecular imaging during and immediately after cancer resections.

This talk will present the methods and procedures used to produce the first results from the Event Horizon Telescope. It is theorized that a black hole will leave a "shadow" on a background of hot gas. Taking a picture of this black hole shadow could help to address a number of important scientific questions, both on the nature of black holes and the validity of general relativity. Unfortunately, due to its small size, traditional imaging approaches require an Earth-sized radio telescope. In this talk, I discuss techniques we have developed to photograph a black hole using the Event Horizon Telescope, a network of telescopes scattered across the globe. Imaging a black hole's structure with this computational telescope requires us to reconstruct images from sparse measurements, heavily corrupted by atmospheric error.